I am planning on doing a project based on a simulation I created. I was wondering if it is allowed to have two experiments under one research project. For example, you use the simulation to find the results of one experiment, and then you use the results of that experiment for a second experiment. And all of this would be under one research project, all the science fair papers would be written for both. Would it make my project more competitive if it had two experiments rather than the usual one?

If the experiments are intimately related and you can carry them out in the allotted time, then I don't see much of a problem. They will enhance your project if you finish them both, and if they both have credible data. It doesn't matter if they confirm or refute your hypothesis as long as the work is done correctly and well.

Ok thanks! I think I might just stick with one and then see how it goes. My simulation is a microsimulation of traffic. Also, I've been doing some reading, but I'm still confused on the difference between floating car data and spatio-temporal data from gps-equipped devices. Do you know the answer?

I gather that floating car data (FCD) means cars that are able to periodically transmit their recent motion data (recent accumulated data on theirpositions (latitude, longitude and altitude) and optionally current speed) to a central data system somewhere that stores and analyzes it. Spatio-temporal data from gps-equipped devices means that it can also send the exact location of the car at regular time intervals, which the FCD data doesn't have directly. You might be able to calculate location from the acceleration data from FCD, but it would probably not be as accurate and you might get drift from inaccuracies and assumptions that would result in incorrect calculated locations. GPS solves that.

hmmm, so if you had say a car that gave it's lat and long every five seconds, would that data being collected be considered spatio-temporal data or floating car data? Also if you only had a starting lat and long, and an ending lat and long, how would you calculate it's acceleration? I get that you can calculate the speed with D/T but is it possible to calculate acceleration based on that?

The GPS data would be spatio-temporal data, lat/long = spatio, every x seconds = temporal. The FCD data apparently only supplies what can be easily captured within the car, such as acceleration and speed. The car doesn't know where it is, and unless it has a compass it probably doesn't even know where it's going. That's why you need the GPS.

You can calculate where a vehicle is and where it's going using only it's starting point, initial direction, and 3D acceleration data, but only if you have very accurate (meaning: expensive) equipment. Intercontinental ballistic missiles (ICBMs) have such systems, which is how they can be more or less accurately targeted. The equipment they use is probably far too expensive for traffic studies though.

I'm programming in Java, and I am trying to do an acceleration and deceleration models for a vehicle. Do you have any suggestions on how to do this? Basically a car will go faster if there's nothing in front of it until it reaches the speed limit. It will go slower if its approaching a car before coming to a stop. I looked at some online, but they are very complicated. Do you know of any simple models?

Hi Probe,You may want to try the search term "Java programming physics instructions" in Google. There's a number of good manual / guide that may help you get started on the programming. In particular, the material from weber.edu and the Open Source Physics website http://www.opensourcephysics.org/search/browse.cfm?browse=gsss looks pretty promising.

What does machine learning do? Input, such as data from databases, is used to create rules or algorithms that make computers (i.e. machines) behave in a certain way. Machine learning is especially important for classification because computers can be used to find patterns easily and even make predictions. The discipline also includes the process of improving such computers.

As for implementing machine learning, which guides have you studied? The following may be particularly helpful: